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. 2023 Apr 5;9(5):443–451. doi: 10.1002/osp4.666

Weight loss maintenance after a digital commercial behavior change program (Noom Weight): Observational cross‐sectional survey study

Christine N May 1,, Matthew Cox‐Martin 1, Annabell Suh Ho 1, Meaghan McCallum 1, Caroline Chan 1, Kelly Blessing 1, Heather Behr 1,2, Paige Blanco 1, Ellen Siobhan Mitchell 1, Andreas Michaelides 1
PMCID: PMC10551118  PMID: 37810531

Abstract

Background

Behavioral weight loss programs often lead to significant short‐term weight loss, but long‐term weight maintenance remains a challenge. Most weight maintenance data come from clinical trials, in‐person programs, or general population surveys, but there is a need for better understanding of long‐term weight maintenance in real‐world digital programs.

Methods

This observational survey study examined weight maintenance reported by individuals who had used Noom Weight, a digital commercial behavior change program, and identified factors associated with greater weight maintenance. The cross‐sectional survey was completed by 840 individuals who had lost at least 10% of their body weight using Noom Weight 6–24 months prior.

Results

The study found that 75% of individuals maintained at least 5% weight loss after 1 year, and 49% maintained 10% weight loss. On average, 65% of initial weight loss was maintained after 1 year and 57% after 2 years. Habitual behaviors, such as healthy snacking and exercise, were associated with greater weight maintenance, while demographic factors were not.

Conclusion

This study provides real‐world data on the long‐term weight maintenance achieved using a fully digital behavioral program. The results suggest that Noom Weight is associated with successful weight maintenance in a substantial proportion of users. Future research will use a randomized controlled trial to track weight maintenance after random assignment and at a 2 year follow‐up.

Keywords: digital health, mHealth, obesity, weight loss maintenance

1. INTRODUCTION

Weight losses of at least 5% and especially 10% of initial body weight can reduce health risks associated with obesity. 1 Comprehensive lifestyle modification targets diet, physical activity, and behavior and can result in 5%–10% weight loss. 2 However, long‐term weight maintenance has proved more elusive. Physiological, environmental, and behavioral factors make it difficult to maintain weight loss long‐term, especially in the absence of structured support. 2 Decades of research show that the majority of individuals regain substantial amounts of weight after intentional weight loss, that most individuals do not maintain 5% or more body weight loss 2 years later, and that up to 80% of the amount of initial weight loss is regained 5 years later, 3 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 leading researchers to call weight maintenance the “greatest challenge in the therapy of obesity.” 12 There is, however, substantial variability such that some proportion of individuals (approximately 20%–40%) are able to maintain at least 5%–10% weight loss 1–2 years after using a program. 4 The exact proportion tends to vary greatly depending on the program or study, highlighting the importance of understanding the amount of maintenance achieved after each program.

Previous studies suggest a range from 20%–31% of participants maintaining 10% weight at 2 years and 17%–50% of participants maintaining 10% weight at 1 year. 7 , 13 , 14 , 15 Review studies report that 54%–67% of initial weight loss is maintained at 1 year (44% at 2 years). 4 , 5 Additionally, two studies of participants in a commercial weight loss program showed that 35%–45% of participants maintained 10% weight at 2 years and 51.6%–54% maintained 10% weight 1 year after finishing the program. 16 , 17 Data from the National Weight Control Registry also show that 88% of participants maintained 10% weight loss at 5 years, using a sample that had already maintained 10% weight loss for 1 year. 18 Research suggests that those who are best able to maintain weight loss tend to engage in regular physical activity and continue to monitor their weight and food intake. 2 , 18

Most of the data on maintenance come from clinical or prospective trials (i.e., formal research or clinical settings) or in‐person commercial programs. 19 However, there is a need to better understand how much weight loss is maintained after use of digital commercial programs in real‐world settings, which could produce different weight maintenance results for a variety of reasons. For instance, individuals need to self‐manage their participation on a fully digital program in the absence of contact from researchers or staff, the knowledge of participating in a prospective research study, and the accountability of attendance in in‐person settings, which are components of clinical trials or in‐person programs that could impact the amount of maintenance achieved. 20 , 21 , 22 , 23 , 24 Along these lines, one study showed that program engagement, which is associated with weight loss and maintenance success, was significantly greater in empirical trials than in real‐world digital use of the same program. 19 , 25 Additionally, another study showed that with monthly personal contact with an interventionist, participants maintained 1.2 kg more weight loss at 30 months than participants exposed to an interactive online experience. 8 Moreover, a meta‐analysis of 10 RCTs reported less long‐term weight maintenance from digital compared to in‐person interventions. 9 Taken together, this evidence highlights the fact that existing data from in‐person or clinical settings may not be generalizable to real‐world use of a digital program, necessitating more data. 25

Therefore, the present study was a cross‐sectional study of individuals who had completed use of a commercial digital behavior change program (Noom Weight) 6–24 months prior to data collection; the aim was to provide a descriptive observation of real‐world weight loss maintenance after this program. Previous studies have found that individuals who are still using Noom Weight at 1 year follow‐up maintain approximately 5% body weight loss, 26 , 27 but an investigation of long‐term maintenance requires individuals to have stopped using the program. The primary research question examined the percentage of individuals maintaining at least 5% and 10% weight loss 6, 12, 18, and 24 months following use of the program, as well as the average amount of weight loss maintained at these time points. A previous study with a similar design examined these outcomes on an in‐person commercial program 1 ; however, the sample may have consisted of a particularly highly motivated sample. Thus, this study aimed to survey a broader sample (i.e., with few inclusion criteria). The previous study also did not evaluate which demographic or behavioral characteristics during maintenance predict the amount of weight maintained, which could help to identify factors to target in order to improve maintenance. Therefore, the secondary research question explored which demographic and behavioral cross‐sectional characteristics during maintenance were associated with the amount of weight maintained.

2. METHODS

2.1. Procedure

This was a cross‐sectional survey study of Noom Weight users who had completed the program 6, 12, 18, and 24 months prior to data collection. Participants were eligible for inclusion if they had used Noom (as defined by performing an in‐app action) for at least the typical length of time for the core program (16 weeks), lost at least 10% of their initial body weight while on Noom, resided in the US, were at least 18 years old, had access to a scale (except a digital Bodytrace scale), and had unsubscribed from the program 5–7, 11–13, 17–19, or 23–25 months ago. Initial weight loss was calculated based on users' self‐reported starting weight and ending weight on the program. Similar to previous work, a 1 month range around each time point was used in order to increase the potential sample size if individuals did not weigh in exactly 6, 12, 18, or 24 months after unsubscribing from Noom. 16 From the program database, a random sample of individuals meeting these criteria (N = 6935) was selected and sent an email invitation to join the study. Participants were ineligible if they had opted out of being contacted by Noom, had become pregnant since completing Noom Weight, were still using Noom Weight, and/or already owned and had set up a Bodytrace digital scale. 855 participants provided informed consent to participate and were compensated $20 and the option to receive a free 16‐week Noom program of their choice for completion of the survey, which asked for individuals' current weight, demographics, and maintenance behaviors. All participants were entered into a lottery to receive a Bodytrace digital scale. Following previous work, a randomly selected subsample (19%, N = 157) were sent digital scales 16 and were compensated an additional $10 for completing a weigh‐in on the digital scale. Efforts to increase survey response rates included sending up to three email invites to sign up for the study and up to three email notifications to complete the survey. All procedures were approved by the Advarra Institutional Review Board.

2.2. Intervention

Noom Weight is a mobile multicomponent behavior change program that has been shown to effectively aid in weight loss of 5% or 10% of initial body weight. 26 , 27 Noom Weight derives its foundation from psychological principles of cognitive behavioral therapy (CBT), acceptance and commitment therapy (ACT), and dialectical behavior therapy (DBT). After downloading the smartphone‐based program from the app store or the website, individuals gain access to a daily curriculum emphasizing behavior change principles and education surrounding healthy eating and physical activity; self‐monitoring features for logging weight, food, and exercise; individualized text message interactions with a human coach; and in‐app community groups. The daily articles emphasize the psychology and common barriers around establishing sustainable and long‐term changes, in contrast to short‐term and unsustainable behaviors. 28

2.3. Measures

2.3.1. Weight maintenance

Weight maintenance was measured in three ways, as in previous work: 16 , 17 (1) the percentage of participants who maintained 5% weight loss, (2) the percentage of participants who maintained 10% weight loss, and (3) the percentage of initial weight loss maintained. These maintenance measures were computed using final weight logged in the program before unsubscribing and current weight. Current weight was derived from survey self‐report and/or the digital scale, which was synced such that data were automatically recorded by the program without the need for manual input. Because there was no significant difference between self‐reported weight measurements and weight measurements objectively measured by the digital scale (p = 0.69), self‐reported weight measurements were used as is unless the participant had objective scale measurements, in which case the objective weight measurement was used.

2.3.2. Maintenance factors

The following behavioral factors during maintenance were measured by self‐report as predictors of maintenance: the extent to which individuals identify with exercise via the Exercise Identity Scale (EIS), physical and mental functioning via the PROMIS‐29 scale (with subscales of physical function, anxiety, depression, fatigue, sleep, social functioning, and pain), habit automaticity via the Self Report Habit Index (SRHI, with subscales of healthy snacking and exercise), and motivation and readiness for change regarding behaviors related to maintaining negative energy balance (e.g., I do not [eat low fat dairy products] at least half the time now but I'm making definite plans to start) via the Multi‐item algorithm of stage of change (MSOC). 29 , 30 , 31 , 32 , 33 Cronbach's alpha for these measures ranged from 0.70 to 0.94, indicating adequate reliability. These variables and scales were all chosen because previous work indicates they are theoretically or empirically related to weight loss maintenance. 34 , 35 , 36 , 37

2.3.3. Engagement

Noom program engagement during the period of weight loss was measured as a predictor of maintenance. Program engagement was not measured during the maintenance period, since maintenance is defined as the time after individuals stopped using the program. Engagement was tracked and measured by the program as the number of days an individual did the following in‐app actions: weighing in, reading an article, messaging a coach, logging a meal or exercise, and posting in a group. Figure 1 depicts the relationship between weight loss and days engaged with the program.

FIGURE 1.

FIGURE 1

Relationship between percent weight lost and days engaged with the program.

2.4. Statistical analysis

Descriptive statistics were used to report the proportion of participants who maintained 5% and 10% weight loss and the average percentage and standard deviation of initial weight loss that was maintained at each time point. Next, a multiple linear regression model was conducted to examine cross‐sectional associations between maintenance factors (independent variables) and the amount of weight maintained (dependent variable). Predictors included all maintenance factors, the number of days engaged on the program (defined as one in‐app action), age, gender, marital status, employment, income, and cohort based on time point (6 months cohort, 12 months cohort, 18 months cohort, or 24 months cohort).

3. RESULTS

3.1. Baseline characteristics

Participants' baseline characteristics are displayed in Table 1.

TABLE 1.

Baseline characteristics.

Characteristic 6 months, N (%) (N = 218) 12 months, N (%) (N = 213) 18 months, N (%) (N = 208) 24 months, N (%) (N = 216)
Gender
Female 177 (83%) 147 (70%) 171 (83%) 171 (81%)
Male 32 (15%) 59 (28%) 34 (17%) 38 (18%)
Other 4 (2%) 3 (1%) 1 (0.5%) 3 (1%)
Employment status
Employed full time 131 (62%) 138 (66%) 137 (66%) 125 (59%)
Employed part time 26 (12%) 17 (8%) 10 (5%) 23 (11%)
Homemaker/not working/student 20 (12%) 19 (9%) 18 (9%) 21 (10%)
Retired 27 (13%) 33 (16%) 38 (19%) 39 (18%)
Other 3 (1%) 3 (1%) 3 (1%) 3 (1%)
Household income
$50,000 or less 28 (13%) 21 (10%) 19 (9%) 30 (14%)
$50,000–$100,000 74 (35%) 62 (30%) 54 (26%) 54 (26%)
$100,000–200,000 51 (24%) 73 (35%) 82 (40%) 61 (29%)
Over $200,000 34 (16%) 28 (14%) 27 (13%) 32 (15%)
Prefer not to say 26 (12%) 23 (11%) 27 (13%) 32 (15%)
Marital status
Married or living with partner 157 (74%) 11 (77%) 163 (80%) 156 (74%)
Divorced/separated 24 (11%) 24 (11%) 19 (9%) 24 (11%)
Widowed 5 (2%) 4 (2%) 4 (2%) 13 (6%)
Never married 23 (11%) 18 (9%) 17 (8%) 12 (6%)
Prefer not to say 4 (2%) 3 (1%) 2 (1%) 6 (3%)
Has children
Yes 56 (27%) 58 (28%) 53 (26%) 52 (25%)
No 154 (73%) 152 (72%) 152 (74%) 159 (75%)

Note: Timepoints in the columns represent 5–7, 11–13, 17–19, and 23–25 months after unsubscribing from the program.

3.2. Amount of weight loss maintenance

3.2.1. Percent of participants maintaining weight loss

The vast majority (82%) of participants maintained at least 5% weight loss at 6 months (75% at 12 months, 58% at 18 months, and 64% at 24 months; Table 2). More than half (56%) of participants maintained 10% weight loss at 6 months (49% at 12 months, 41% at 18 months, and 42% at 24 months).

TABLE 2.

Amount of weight loss maintenance per timepoint.

Statistic 6 months, N (%) or M (SD) 12 months, N (%) or M (SD) 18 months, N (%) or M (SD) 24 months, N (%) or M (SD)
Percent maintaining 5% weight loss, % (N) (172/210) 82% (156/208) 75% (114/196) 58% (135/210) 64%
Percent maintaining 10% weight loss, % (N) (118/210) 56% (102/208) 49% (81/196) 41% (88/210) 42%
Average percent of initial weight loss maintained, M (SD) 73% (45%) 65% (47%) 48% (46%) 57% (57%)

3.2.2. Percent of initial weight loss maintained

Out of their initial weight loss, on average, participants maintained 73% (SD = 45%) at 6 months, 65% (SD = 47%) at 12 months, 48% (SD = 46%) at 18 months, and 57% (SD = 57%) at 24 months.

3.3. Associations between maintenance factors and weight loss maintenance

A multivariate regression found that habitual healthy snacking (B = 0.18, s.e. = 0.06, p = 0.002), habitual exercise (B = 0.18, s.e. = 0.07, p = 0.001), and motivation toward change in terms of portion size (B = 2.87, s.e. = 0.48, p < 0.001) during maintenance were positively associated with greater amounts of weight loss maintained. Other maintenance factors were not significantly associated with the amount of weight loss maintained, though physical functioning (B = 0.41, s.e. = 0.21, p = 0.055), change motivation for physical activity (B = −0.81, s.e. = 0.42, p = 0.054), and change motivation for planned exercise (B = 0.53, s.e. = 0.29, p = 0.07) were marginally significantly associated with the amount of weight maintained. The number of days individuals were on the program was positively associated with weight loss maintenance (B = 0.01, s.e. = 0.002, p < 0.001) (See a depiction of average engagement per week with regards to specific program components in Table 3). The significant demographic predictors were male gender (B = 1.78, s.e. = 0.74, p = 0.02), income of less than $25,000 (B = 4.78, s.e. = 2.11, p = 0.02) and between $25,000–50,000 (B = 2.60, s.e. = 1.11, p = 0.02) compared to $100,000–$200,000 (Table 4). These demographic factors were all positively associated with the amount of weight loss maintained.

TABLE 3.

Descriptive statistics for weekly program engagement.

Engagement category Min Max Median Mean SD
Coach messages 0 45 0 0.87 1.67
Articles read 0 110 4 10.56 12.23
Exercises logged 0 37 0 2.73 3.79
Meals logged 0 48 18 16.13 13.35
Group posts 0 19 0 0.25 1.05
Weigh ins 0 23 5 4.06 2.95
Water logs 0 50 0 1.04 3.06

TABLE 4.

Demographic and maintenance‐related predictors of weight loss maintenance.

Predictor Coefficient (s.e.) t‐value p‐value
Number of days engaged 0.01 (0.002) 4.86 <0.001
Age −0.005 (0.03) −0.21 0.84
Male (vs. female) 1.78 (0.74) 2.39 0.02
Transgender (vs. female) −0.20 (5.71) −0.03 0.97
Never married (vs. married) 0.61 (1.14) 0.53 0.59
Divorced/separated (vs. married) −0.23 (0.97) −0.24 0.81
Widowed (vs. married) 2.65 (1.80) 1.47 0.14
Number of children −0.99 (0.74) −1.33 0.18
Not working (vs. employed full time) −2.11 (2.66) −0.79 0.43
Retired (vs. employed full time) 0.15 (0.96) 0.15 0.88
Homemaker (vs. employed full time) −0.53 (1.32) −0.40 0.69
Employed part time (vs. employed full time) −0.67 (1.04) −0.64 0.52
Volunteer (vs. employed full time) 0.09 (3.18) 0.03 0.98
Disabled (vs. employed full time) −2.59 (3.62) −0.71 0.47
Student (vs. employed full time) −0.84 (1.89) −0.44 0.66
Less than $25,000/year household income (vs. $100,000–200,000) 4.78 (2.11) 2.27 0.02
$25,000–$50,000 household income (vs. $100,000–200,000) 2.60 (1.11) 2.35 0.02
$50,000–$100,000 household income (vs. $100,000–200,000) 0.64 (0.75) 0.85 0.40
Over $200,000 household income (vs. $100,000–200,000) 0.29 (0.91) 0.31 0.75
Exercise identity (measured by exercise identity scale) −0.03 (0.03) −1.07 0.28
Physical function (measured by PROMIS) 0.41 (0.21) 1.92 0.055
Anxiety (measured by PROMIS) 0.04 (0.14) 0.28 0.78
Depression (measured by PROMIS) −0.23 (0.14) −1.62 0.11
Fatigue (measured by PROMIS) −0.19 (0.12) −1.55 0.12
Sleep (measured by PROMIS) 0.09 (0.16) 0.57 0.57
Social functioning (measured by PROMIS) −0.14 (0.13) −1.07 0.28
Pain (measured by PROMIS) 0.001 (0.08) 0.13 0.90
Automaticity of healthy snacking (measured by Self Report Habit Index) 0.18 (0.06) 3.16 0.002
Automaticity of exercise (measured by Self Report Habit Index) 0.18 (0.07) 2.58 0.01
Readiness for change of portion size (measured by MSOC) 2.87 (0.48) 5.95 <0.001
Readiness for change for dietary fat (measured by MSOC) 0.48 (0.38) 1.28 0.20
Readiness for change for fruit and vegetable intake (measured by MSOC) −0.07 (0.43) −0.16 0.87
Readiness for change for physical activity (measured by MSOC) −0.81 (0.42) −1.93 0.054
Readiness for change for planned exercise (measured by MSOC) 0.53 (0.29) 1.83 0.07

Note: Predictors were entered simultaneously in a multiple linear regression model.

Abbreviation: MSOC, multi‐item algorithm of stage of change.

4. DISCUSSION

A cross‐sectional survey of previous Noom Weight users was conducted to understand how much maintenance had been achieved, as well as what demographic and behavioral factors during maintenance were associated with the amount of weight loss maintained. Results showed that since commencing Noom Weight, 75% of participants maintained 5% weight loss at 1 year (64% at 2 years and 82% at 6 months). Moreover, 49% maintained at least 10% weight loss at 1 year (42% at 2 years and 56% at 6 months). On average, 65% of the amount of initial weight loss was maintained 1 year later (57% at 2 years and 73% at 6 months).

Our results compare favorably to results from previous studies, 7 , 13 , 14 , 38 as well as to results with more selective samples from WW or the National Weight Control Registry. 15 , 16 , 17 In a well‐powered randomized controlled trial, future research will investigate which specific behavior change techniques and program components of Noom were responsible for the amount of maintenance achieved. 18 In addition to the behavior change techniques (e.g., self‐monitoring) and healthy eating and physical activity behaviors emphasized by the program, which are common to many weight management programs, it is possible that the considerable maintenance achieved was due to the cognitive behavioral therapy (CBT) and acceptance‐commitment therapy (ACT)‐based components of the program that helped to encourage cognitive flexibility. Cognitive flexibility allows individuals to move from black‐and‐white thinking surrounding weight loss and behavioral change toward more sustainable and accepting thought patterns, even in the midst of negative thoughts or feelings. 39 Our speculation is based on qualitative user feedback and on research showing that cognitive flexibility is associated with higher maintenance. 11 , 40

The number of days engaged on the program, as well as demographic characteristics of male gender and a household income of less than $25k and $25–50k (compared to $100k) were associated with greater maintenance, whereas age, employment, marital status, and presence of children showed no association. Our results suggest that greater participation in the program was positively associated with greater maintenance. This overall measure included both engaging in program‐specific components (e.g., program‐specific articles) as well as maintenance‐related behaviors that were measured by the program, such as self‐monitoring of exercise and food intake. The findings suggest that the longer participants used the program, the better their maintenance success even when accounting for behaviors during maintenance. In terms of demographic factors, our results mostly corroborate a systematic review which suggested that age, gender, socioeconomic status (including income and occupational status), and marital status are not associated with weight loss maintenance. On the other hand, our results for gender align with another review suggesting greater weight loss (and maintenance) success among males. 19 Our findings highlight the need for more research in non‐clinical or in‐person samples, which could show varied patterns compared to commonly studied clinical samples. For example, it is possible that fully digital programs better enable success among males who report reticence toward regular attendance at in‐person weight programs. 41 , 42 , 43 Future research should test these lines of speculation.

Further, portion control, habitual exercise, and habitual healthy snack choice during maintenance, as measured by the EIS, PROMIS‐29, SRHI, and MSOC questionnaires, were positively associated with the amount of weight maintained. This corroborates work showing that portion control, physical activity, and healthy snack choice during maintenance are strongly associated with greater maintenance. 34 Automatic or habitual performance of these behaviors has been studied less in the context of weight maintenance, despite the association between habitual behaviors, as measured by the SRHI, and health behaviors. 44 Related research to date suggests that habit‐focused interventions show higher SRHI scores and better weight maintenance than control groups, 37 maintenance of other health behaviors (i.e., not weight loss‐related, such as medication adherence) are associated with habitual behaviors, 45 and there are theoretical links between habitual behaviors and weight loss maintenance. 46 , 47 To our knowledge, this is the first study to show that on a generalized (i.e., not primarily focused on automatic habit formation) program, habitual exercise and healthy snack choice were positively associated with weight loss maintenance. Future studies should consider this factor and evaluate it when trying to understand and predict weight loss maintenance.

We did not measure factors like usability or acceptability of the program in this study, but these factors could also explain the high level of maintenance achieved, as research on another Noom program showed high levels of usability and acceptability. 48 The Noom programs have utilized user experience research and experimentation to increase usability, which could amplify engagement and weight loss management more than alternatives with less usability and acceptability. Future research should assess how usability and acceptability directly influence engagement and maintenance.

A strength of the study is that its design allows for as close to real‐world data as possible, as these were real‐world Noom consumers who responded to the survey in the absence of a clinical setting, continual researcher contact, or salient long‐term or repeated‐measure research study participation. There are, however, several limitations to this approach. One limitation is that, as with all survey studies, only those who responded to the survey were included. This means that it is possible that only the most motivated individuals or those who had the most maintenance responded to the survey. However, our response rate of 12% is in line with typical online survey rates 49 and many participants reported little or no weight loss maintenance, which rules out the possibility that only those with substantial maintenance success responded to the survey. Nevertheless, a future study will use a randomized controlled trial to follow all participants who started the intervention to calculate weight maintenance from the whole sample. Another limitation is the reliance on self‐reported weight rather than objectively measured weight. To address this and still observe real‐world data, a random subsample of participants were provided digital scales, whose data were automatically synced and therefore did not require self‐report. Self‐reported data did not significantly differ from objective data from the digital scales in this study, but future studies should validate all weight measurements. Another limitation to this design was the inability to measure some behavioral factors (e.g., habitual exercise) throughout the intervention, rather than only at one time during the maintenance period. A final limitation is that it is unknown whether participants used any other weight treatments or medications during the weight loss or maintenance periods. Future studies will measure behavioral factors over time as well as gather data with regards to weight treatments or medications throughout the course of the study.

This study contributes important data to the limited evidence base on weight maintenance from a digital commercial program. This cross‐sectional survey study found that the majority (75%) of previous Noom users who had lost 10% body weight on the program maintained 5% weight at 1 year (64% at 2 years). In addition, 49% of previous Noom users maintained 10% weight at 1 year (42% at 2 years). Further, portion control, habitual physical activity, and habitual healthy snack choice during maintenance, as well as the number of days engaged on the program, were positively associated with the amount of weight loss individuals maintained. Future research should extend these results with randomized designs, by evaluating the contribution of habitual behaviors in weight loss maintenance, and by examining which specific program components drive the relationship between general engagement on this program and weight loss maintenance.

CONFLICT OF INTEREST STATEMENT

Authors C.N.M., M.C.‐M., A.S.H., M.M., C.C., K.B., P.B., and A.M. are employees at Noom Inc. In the Academic Research department and have received salary and stock options for their employment. Authors H.B. and E.S.M. were employees at Noom at the time of the manuscript. This study was funded by Noom, which did not (other than the specific authors listed above) play a role in the study's design, execution, analyses, interpretation of the data, or the decision to publish the results.

ACKNOWLEDGMENTS

C.N.M., M.M., and E.S.M. conceived the study. M.M., C.C., K.B., H.B., and P.B. carried out study management. M.C.‐M. analyzed data. A.S.H. wrote the paper. A.M. provided supervision. All authors were involved in revising the paper and had final approval of the submitted version.

May CN, Cox‐Martin M, Ho AS, et al. Weight loss maintenance after a digital commercial behavior change program (Noom Weight): observational cross‐sectional survey study. Obes Sci Pract. 2023;9(5):443‐451. 10.1002/osp4.666

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